Which type of data is most commonly used to train a predictive model for customer churn?

Real-time sensor data from IoT devices.

Randomly generated synthetic data.

Historical customer behavior data, such as usage patterns and support interactions.

Unstructured text from social media only.

How does predictive AI differ from generative AI?

Predictive AI is used only in business, while generative AI is used only in creative fields.

Predictive AI forecasts future events, while generative AI produces new content.

Predictive AI requires labeled data, while generative AI always uses unlabeled data.

Predictive AI creates new data instances, while generative AI analyzes existing data.

What is a critical data quality requirement for training effective predictive AI models?

Data must be collected in real time to be useful.

Data must be stored in a cloud data warehouse.

Data must be free of errors, missing values, and inconsistencies.

Data must be labeled by a single expert to ensure consistency.

A credit card company uses a predictive model to approve or decline transactions. Recently, the model has been declining more legitimate transactions than expected. What is the most likely challenge with predictive AI that is causing this issue?

Data drift, because customer spending patterns have changed over time

Overfitting, because the model memorized the training data exactly

Generative AI, because the model is generating new transactions

Interpretability, because the model cannot explain its decisions

Which scenario is a common use case for predictive AI?

Forecasting customer demand for inventory planning.

Translating documents from English to Japanese.

Summarizing a long report into a short paragraph.

Generating realistic product images for an online catalog.

NetAppCertified AI Expert

Domain 2Objective 2

Describe the Impact of Predictive AI CERTIFIED-AI-EXPERT Practice Questions (Page 3)

Part of the AI Lifecycle domain, which accounts for 27% of the CERTIFIED-AI-EXPERT exam. NetApp does not publish an official question count, but from its 90-minute exam (~35–60 total, ~9–16 in this domain), expect 1–2 from this objective — we provide 27 practice questions to prepare you well beyond it. (estimate)

27questions here

6free pages

6concepts

27%of the exam

Questions 11–15

11foundation · easy
Which type of data is most commonly used to train a predictive model for customer churn?
Select an answer first
12foundation · easy
How does predictive AI differ from generative AI?
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13foundation · easy
What is a critical data quality requirement for training effective predictive AI models?
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14application · medium
A credit card company uses a predictive model to approve or decline transactions. Recently, the model has been declining more legitimate transactions than expected. What is the most likely challenge with predictive AI that is causing this issue?
Select an answer first
15foundation · easy
Which scenario is a common use case for predictive AI?
Select an answer first

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